lang-limes 0.1.2

Linguistic Identification of Morphosyntactic and Expressive Snags

LIMES: Linguistic Identification of Morphosyntactic and Expressive Snags

LIMES is a library for performing linguistic analyses on provided texts regarding their complexity. The goal of this project is to create a tool that provides actionable insights on how to make written texts easier to comprehend.

Refer to the project documentation for in-depth information about concepts, API, and more.

Please note that the actual logic for identifying language barriers is completely language-specific. Because it is a lot of work to develop these heuristics, the library currently only ships with implemented analyzers for German texts. However, we encourage you to build your own analyzers based on the provided class templates, either for your own use or to contribute to the project.

Installation

You can install this package via pip by running:

pip install lang-limes

Additional Dependencies

The library requires use of a Parser. Currently, we only ship a parser based on spaCy's excellent NLP pipeline. This means that you need to install a spaCy model that supports the language you are working with.

Example Usage

You must use a string container to wrap the text you want to analyze. As our analysis work on a sentence level, you can either manually sentencize and create separate Sentence objects or just throw your whole text into a Text object that takes care of sentencization for you.

We will do the latter for the purpose of this example.

from limes import Text
from limes.parsers.spacy_parser import SpacyParser
from limes.analyzers.de import GermanAnalyzer

analyzer = GermanAnalyzer()

# You can also pass a spacy NLP object instead of the model name.
# Make sure the model you want to use is installed.
parser = SpacyParser(model="de_core_news_sm")

text = Text(
    raw="Das hier ist ein Text. Dieser Text hat mehrere Sätze.",
    analyzer=analyzer,
    parser=parser,
)

For parsing, we recommend using the most powerful model that your system can reasonably run, as barrier detection is very sensitive to errors during morpho- syntactic analysis. We've used the small model in the example above but if you can, try using the transformer-based de-dep-news-trf for improved results at the cost of compute time.

Identifying Barriers

Barriers are detected lazily, and results are cached to avoid redundant computations. Barriers themselves are a property of the Text object.

# You can iterate over the all barriers in the entire text if you want.
for barrier in text.barriers:
    print(barrier.title)
    # Print the actual string of the token.
    print(barrier.affected_tokens)
    # Print the position of the token in the source text.
    if barrier.affected_tokens is not None:
        print([token.i for token in barrier.affected_tokens])

# You can also iterate over each sentence.
for sentence in text:
    print(sentence.barriers)

# Alternatively, you can also inspect a specific sentence by index.
print(text[1].barriers)

Please note that barriers are also language-specific (because different languages also differ in how they make comprehension "difficult").

Calculating Complexities

There are multiple ways in which you can try to approximate language complexity (see our documentation for more information).

from limes import ComplexityAlgorithm

# Get the average complexity of the text. You can manually set the heuristic.
avg_complexity = text.average_complexity(
    heuristic=ComplexityAlgorithm.AGGREGATED_LOCAL,
)
print(avg_complexity)

# Alternatively, you can get phrase-level complexities.
# These are also lazily computed and cached.
for phrase, complexity in text.local_complexities:
    print(phrase)
    print(complexity)

# You could also iterate over all sentences in the text and get each sentence's
# global complexity.
for sentence in text:
    complexity = sentence.global_complexity(
        heuristic=ComplexityAlgorithm.AGGREGATED_LOCAL,
    )
    print(sentence)
    print(complexity)

Next Steps

A good place to start is to get an overview of the concepts used to build and configure the whole processing pipeline.

Currently Supported Languages

Language	Contributors
DE	Katja Grosch & Susanne Wagner (IFTO GmbH), Jannik Schmitt (deepsight GmbH)

Additional Resources

Word Frequency Lists

German

The frequency list for German words was kindly provided by Projekt Deutscher Wortschatz of the Universität Leipzig. The unprocessed list included in this repository (data/deu_words_2024.txt) is based on [1]. Please note that it is not based on the publicly available "Normgrößenkorpora" but was provided on request by the Leipzig Corpora team under a CC BY 4.0 license.

References

[1] Leipzig Corpora Collection (2024). German news corpus based on material from 2024. Leipzig Corpora Collection. Dataset. https://corpora.uni-leipzig.de/en?corpusId=deu_news_2024